Overview

Dataset statistics

Number of variables14
Number of observations348283
Missing cells53854
Missing cells (%)1.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory37.2 MiB
Average record size in memory112.0 B

Variable types

Categorical1
DateTime3
Numeric9
Text1

Alerts

VERSIE has constant value ""Constant
DATUM_BESTAND has constant value ""Constant
PEILDATUM has constant value ""Constant
GEMIDDELDE_VERKOOPPRIJS has 53854 (15.5%) missing valuesMissing
AANTAL_SUBTRAJECT_PER_ZPD is highly skewed (γ1 = 21.35699905)Skewed

Reproduction

Analysis started2024-01-08 15:06:28.890315
Analysis finished2024-01-08 15:06:45.452753
Duration16.56 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

VERSIE
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.7 MiB
1.0
348283 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1044849
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 348283
100.0%

Length

2024-01-08T15:06:45.544963image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-08T15:06:45.814694image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
1.0 348283
100.0%

Most occurring characters

ValueCountFrequency (%)
1 348283
33.3%
. 348283
33.3%
0 348283
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 696566
66.7%
Other Punctuation 348283
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 348283
50.0%
0 348283
50.0%
Other Punctuation
ValueCountFrequency (%)
. 348283
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1044849
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 348283
33.3%
. 348283
33.3%
0 348283
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1044849
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 348283
33.3%
. 348283
33.3%
0 348283
33.3%

DATUM_BESTAND
Date

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.7 MiB
Minimum2023-12-08 00:00:00
Maximum2023-12-08 00:00:00
2024-01-08T15:06:45.928145image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-08T15:06:46.061325image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

PEILDATUM
Date

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.7 MiB
Minimum2023-12-01 00:00:00
Maximum2023-12-01 00:00:00
2024-01-08T15:06:46.184243image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-08T15:06:46.317429image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

JAAR
Date

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.7 MiB
Minimum2012-01-01 00:00:00
Maximum2023-01-01 00:00:00
2024-01-08T15:06:46.440755image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-08T15:06:46.591332image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
Distinct28
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean451.5876
Minimum301
Maximum8418
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.7 MiB
2024-01-08T15:06:46.758918image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum301
5-th percentile302
Q1305
median313
Q3322
95-th percentile361
Maximum8418
Range8117
Interquartile range (IQR)17

Descriptive statistics

Standard deviation1041.0915
Coefficient of variation (CV)2.3054033
Kurtosis54.470481
Mean451.5876
Median Absolute Deviation (MAD)8
Skewness7.5099326
Sum1.5728028 × 108
Variance1083871.6
MonotonicityNot monotonic
2024-01-08T15:06:46.949308image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
305 48900
14.0%
313 45230
13.0%
303 40083
11.5%
330 27420
 
7.9%
316 23684
 
6.8%
308 18857
 
5.4%
306 14642
 
4.2%
324 14315
 
4.1%
301 13901
 
4.0%
304 11326
 
3.3%
Other values (18) 89925
25.8%
ValueCountFrequency (%)
301 13901
 
4.0%
302 7656
 
2.2%
303 40083
11.5%
304 11326
 
3.3%
305 48900
14.0%
306 14642
 
4.2%
307 6120
 
1.8%
308 18857
 
5.4%
310 3802
 
1.1%
313 45230
13.0%
ValueCountFrequency (%)
8418 4683
 
1.3%
8416 1156
 
0.3%
1900 229
 
0.1%
390 957
 
0.3%
389 3650
 
1.0%
362 4469
 
1.3%
361 2538
 
0.7%
335 3505
 
1.0%
330 27420
7.9%
329 909
 
0.3%
Distinct1902
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size2.7 MiB
2024-01-08T15:06:47.340763image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length3.3530405
Min length2

Characters and Unicode

Total characters1167807
Distinct characters25
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10 ?
Unique (%)< 0.1%

Sample

1st row11
2nd row11
3rd row13
4th row12
5th row11
ValueCountFrequency (%)
101 1485
 
0.4%
402 1428
 
0.4%
403 1400
 
0.4%
301 1397
 
0.4%
201 1327
 
0.4%
203 1298
 
0.4%
401 1169
 
0.3%
404 1158
 
0.3%
409 1128
 
0.3%
802 1126
 
0.3%
Other values (1892) 335367
96.3%
2024-01-08T15:06:47.950491image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 223286
19.1%
0 214598
18.4%
2 154768
13.3%
3 126335
10.8%
5 90131
7.7%
9 84059
 
7.2%
4 82672
 
7.1%
7 68820
 
5.9%
6 61006
 
5.2%
8 50327
 
4.3%
Other values (15) 11805
 
1.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1156002
99.0%
Uppercase Letter 11805
 
1.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
G 2198
18.6%
M 1990
16.9%
B 1429
12.1%
Z 1030
8.7%
E 987
8.4%
D 778
 
6.6%
A 763
 
6.5%
F 732
 
6.2%
C 388
 
3.3%
K 379
 
3.2%
Other values (5) 1131
9.6%
Decimal Number
ValueCountFrequency (%)
1 223286
19.3%
0 214598
18.6%
2 154768
13.4%
3 126335
10.9%
5 90131
7.8%
9 84059
 
7.3%
4 82672
 
7.2%
7 68820
 
6.0%
6 61006
 
5.3%
8 50327
 
4.4%

Most occurring scripts

ValueCountFrequency (%)
Common 1156002
99.0%
Latin 11805
 
1.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
G 2198
18.6%
M 1990
16.9%
B 1429
12.1%
Z 1030
8.7%
E 987
8.4%
D 778
 
6.6%
A 763
 
6.5%
F 732
 
6.2%
C 388
 
3.3%
K 379
 
3.2%
Other values (5) 1131
9.6%
Common
ValueCountFrequency (%)
1 223286
19.3%
0 214598
18.6%
2 154768
13.4%
3 126335
10.9%
5 90131
7.8%
9 84059
 
7.3%
4 82672
 
7.2%
7 68820
 
6.0%
6 61006
 
5.3%
8 50327
 
4.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1167807
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 223286
19.1%
0 214598
18.4%
2 154768
13.3%
3 126335
10.8%
5 90131
7.7%
9 84059
 
7.2%
4 82672
 
7.1%
7 68820
 
5.9%
6 61006
 
5.2%
8 50327
 
4.3%
Other values (15) 11805
 
1.0%

ZORGPRODUCT_CD
Real number (ℝ)

Distinct6231
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.4125155 × 108
Minimum10501002
Maximum9.9841808 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.7 MiB
2024-01-08T15:06:48.175930image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum10501002
5-th percentile28999040
Q199899009
median1.4959903 × 108
Q39.9000302 × 108
95-th percentile9.9051605 × 108
Maximum9.9841808 × 108
Range9.8791708 × 108
Interquartile range (IQR)8.9010402 × 108

Descriptive statistics

Standard deviation4.2904666 × 108
Coefficient of variation (CV)0.9723403
Kurtosis-1.7393726
Mean4.4125155 × 108
Median Absolute Deviation (MAD)1.1960003 × 108
Skewness0.46549346
Sum1.5368041 × 1014
Variance1.8408104 × 1017
MonotonicityNot monotonic
2024-01-08T15:06:48.392097image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
990004009 2538
 
0.7%
990004007 2496
 
0.7%
990003004 2411
 
0.7%
990004006 2038
 
0.6%
990356076 1855
 
0.5%
990356073 1724
 
0.5%
131999228 1704
 
0.5%
131999164 1681
 
0.5%
990003007 1559
 
0.4%
131999194 1534
 
0.4%
Other values (6221) 328743
94.4%
ValueCountFrequency (%)
10501002 9
< 0.1%
10501003 12
< 0.1%
10501004 12
< 0.1%
10501005 12
< 0.1%
10501007 3
 
< 0.1%
10501008 12
< 0.1%
10501010 12
< 0.1%
10501011 3
 
< 0.1%
11101002 11
< 0.1%
11101003 12
< 0.1%
ValueCountFrequency (%)
998418081 178
0.1%
998418080 161
< 0.1%
998418079 40
 
< 0.1%
998418077 9
 
< 0.1%
998418076 9
 
< 0.1%
998418075 7
 
< 0.1%
998418074 240
0.1%
998418073 241
0.1%
998418072 9
 
< 0.1%
998418071 9
 
< 0.1%

AANTAL_PAT_PER_ZPD
Real number (ℝ)

Distinct10529
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean514.81051
Minimum1
Maximum165184
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.7 MiB
2024-01-08T15:06:48.592183image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median14
Q3103
95-th percentile1747.9
Maximum165184
Range165183
Interquartile range (IQR)100

Descriptive statistics

Standard deviation3186.1044
Coefficient of variation (CV)6.1888876
Kurtosis413.453
Mean514.81051
Median Absolute Deviation (MAD)13
Skewness16.802664
Sum1.7929975 × 108
Variance10151261
MonotonicityNot monotonic
2024-01-08T15:06:48.797181image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 57632
 
16.5%
2 28139
 
8.1%
3 18421
 
5.3%
4 13446
 
3.9%
5 10519
 
3.0%
6 8920
 
2.6%
7 7406
 
2.1%
8 6238
 
1.8%
9 5678
 
1.6%
10 5097
 
1.5%
Other values (10519) 186787
53.6%
ValueCountFrequency (%)
1 57632
16.5%
2 28139
8.1%
3 18421
 
5.3%
4 13446
 
3.9%
5 10519
 
3.0%
6 8920
 
2.6%
7 7406
 
2.1%
8 6238
 
1.8%
9 5678
 
1.6%
10 5097
 
1.5%
ValueCountFrequency (%)
165184 1
< 0.1%
162460 1
< 0.1%
161431 1
< 0.1%
155869 1
< 0.1%
154540 1
< 0.1%
154258 1
< 0.1%
144714 1
< 0.1%
118396 1
< 0.1%
115935 1
< 0.1%
113249 1
< 0.1%

AANTAL_SUBTRAJECT_PER_ZPD
Real number (ℝ)

SKEWED 

Distinct11324
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean610.12209
Minimum1
Maximum240002
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.7 MiB
2024-01-08T15:06:48.999166image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median15
Q3113
95-th percentile1994
Maximum240002
Range240001
Interquartile range (IQR)110

Descriptive statistics

Standard deviation4113.8514
Coefficient of variation (CV)6.7426692
Kurtosis723.91859
Mean610.12209
Median Absolute Deviation (MAD)14
Skewness21.356999
Sum2.1249515 × 108
Variance16923773
MonotonicityNot monotonic
2024-01-08T15:06:49.208700image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 55495
 
15.9%
2 27644
 
7.9%
3 18238
 
5.2%
4 13255
 
3.8%
5 10430
 
3.0%
6 8888
 
2.6%
7 7345
 
2.1%
8 6170
 
1.8%
9 5596
 
1.6%
10 5115
 
1.5%
Other values (11314) 190107
54.6%
ValueCountFrequency (%)
1 55495
15.9%
2 27644
7.9%
3 18238
 
5.2%
4 13255
 
3.8%
5 10430
 
3.0%
6 8888
 
2.6%
7 7345
 
2.1%
8 6170
 
1.8%
9 5596
 
1.6%
10 5115
 
1.5%
ValueCountFrequency (%)
240002 1
< 0.1%
232423 1
< 0.1%
231954 1
< 0.1%
230939 1
< 0.1%
227936 1
< 0.1%
227409 1
< 0.1%
226314 1
< 0.1%
223891 1
< 0.1%
218673 1
< 0.1%
215131 1
< 0.1%

AANTAL_PAT_PER_DIAG
Real number (ℝ)

Distinct9478
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7724.599
Minimum1
Maximum232594
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.7 MiB
2024-01-08T15:06:49.401178image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile41
Q1407
median1728
Q36359
95-th percentile36952
Maximum232594
Range232593
Interquartile range (IQR)5952

Descriptive statistics

Standard deviation17938.902
Coefficient of variation (CV)2.3223084
Kurtosis34.821336
Mean7724.599
Median Absolute Deviation (MAD)1576
Skewness5.0992958
Sum2.6903465 × 109
Variance3.2180419 × 108
MonotonicityNot monotonic
2024-01-08T15:06:49.598339image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
21 581
 
0.2%
8 513
 
0.1%
25 512
 
0.1%
9 512
 
0.1%
19 506
 
0.1%
23 504
 
0.1%
17 488
 
0.1%
14 482
 
0.1%
12 479
 
0.1%
4 469
 
0.1%
Other values (9468) 343237
98.6%
ValueCountFrequency (%)
1 386
0.1%
2 458
0.1%
3 445
0.1%
4 469
0.1%
5 437
0.1%
6 451
0.1%
7 438
0.1%
8 513
0.1%
9 512
0.1%
10 416
0.1%
ValueCountFrequency (%)
232594 22
< 0.1%
230662 23
< 0.1%
227999 23
< 0.1%
218496 24
< 0.1%
214506 17
< 0.1%
213515 25
< 0.1%
211579 17
< 0.1%
210414 19
< 0.1%
205337 17
< 0.1%
200600 16
< 0.1%
Distinct10607
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11197.494
Minimum1
Maximum370139
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.7 MiB
2024-01-08T15:06:49.788786image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile52
Q1538
median2397
Q39161
95-th percentile52238
Maximum370139
Range370138
Interquartile range (IQR)8623

Descriptive statistics

Standard deviation26927.005
Coefficient of variation (CV)2.4047349
Kurtosis38.235007
Mean11197.494
Median Absolute Deviation (MAD)2202
Skewness5.3387873
Sum3.8998968 × 109
Variance7.250636 × 108
MonotonicityNot monotonic
2024-01-08T15:06:49.995692image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
25 433
 
0.1%
17 416
 
0.1%
24 414
 
0.1%
23 409
 
0.1%
33 405
 
0.1%
6 389
 
0.1%
4 382
 
0.1%
39 382
 
0.1%
21 378
 
0.1%
20 378
 
0.1%
Other values (10597) 344297
98.9%
ValueCountFrequency (%)
1 298
0.1%
2 340
0.1%
3 367
0.1%
4 382
0.1%
5 350
0.1%
6 389
0.1%
7 357
0.1%
8 345
0.1%
9 310
0.1%
10 374
0.1%
ValueCountFrequency (%)
370139 23
< 0.1%
365383 23
< 0.1%
358372 22
< 0.1%
348482 25
< 0.1%
344611 24
< 0.1%
341651 19
< 0.1%
323753 20
< 0.1%
315771 17
< 0.1%
310754 17
< 0.1%
298627 17
< 0.1%

AANTAL_PAT_PER_SPC
Real number (ℝ)

Distinct325
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean671239.57
Minimum1610
Maximum1487630
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.7 MiB
2024-01-08T15:06:50.202192image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1610
5-th percentile41749
Q1287320
median757844
Q31030951
95-th percentile1332293
Maximum1487630
Range1486020
Interquartile range (IQR)743631

Descriptive statistics

Standard deviation413732.12
Coefficient of variation (CV)0.61637027
Kurtosis-1.131107
Mean671239.57
Median Absolute Deviation (MAD)315289
Skewness0.0062439679
Sum2.3378133 × 1011
Variance1.7117426 × 1011
MonotonicityNot monotonic
2024-01-08T15:06:50.413195image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
880927 5102
 
1.5%
874084 4354
 
1.3%
843977 4347
 
1.2%
894304 4333
 
1.2%
880463 4273
 
1.2%
897696 4212
 
1.2%
765012 4089
 
1.2%
803553 4029
 
1.2%
791223 4004
 
1.1%
1050964 3909
 
1.1%
Other values (315) 305631
87.8%
ValueCountFrequency (%)
1610 130
 
< 0.1%
1829 138
 
< 0.1%
1919 131
 
< 0.1%
2495 173
< 0.1%
2556 190
0.1%
4205 177
0.1%
4406 115
 
< 0.1%
6804 380
0.1%
7794 72
 
< 0.1%
9808 358
0.1%
ValueCountFrequency (%)
1487630 2975
0.9%
1450392 3048
0.9%
1421700 3564
1.0%
1344233 3543
1.0%
1340550 3441
1.0%
1332293 3545
1.0%
1316326 3463
1.0%
1300016 3394
1.0%
1282931 3576
1.0%
1267070 3350
1.0%

AANTAL_SUBTRAJECT_PER_SPC
Real number (ℝ)

Distinct325
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1089131.1
Minimum1861
Maximum2664062
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.7 MiB
2024-01-08T15:06:50.625894image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1861
5-th percentile46332
Q1404601
median1087877
Q31810515
95-th percentile2577611
Maximum2664062
Range2662201
Interquartile range (IQR)1405914

Descriptive statistics

Standard deviation747430.91
Coefficient of variation (CV)0.68626346
Kurtosis-0.7863428
Mean1089131.1
Median Absolute Deviation (MAD)684872
Skewness0.37835987
Sum3.7932586 × 1011
Variance5.5865297 × 1011
MonotonicityNot monotonic
2024-01-08T15:06:50.838953image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1211797 5102
 
1.5%
1281478 4354
 
1.3%
1216252 4347
 
1.2%
1315563 4333
 
1.2%
1300429 4273
 
1.2%
1341815 4212
 
1.2%
1155932 4089
 
1.2%
1205561 4029
 
1.2%
1177018 4004
 
1.1%
2577611 3909
 
1.1%
Other values (315) 305631
87.8%
ValueCountFrequency (%)
1861 130
 
< 0.1%
2097 138
 
< 0.1%
2194 131
 
< 0.1%
2816 173
< 0.1%
3324 190
0.1%
4796 115
 
< 0.1%
5035 177
0.1%
7383 380
0.1%
8244 72
 
< 0.1%
10667 358
0.1%
ValueCountFrequency (%)
2664062 3866
1.1%
2663359 3793
1.1%
2618463 3789
1.1%
2593332 3843
1.1%
2577611 3909
1.1%
2548052 3890
1.1%
2479845 3851
1.1%
2178429 3757
1.1%
2062095 3811
1.1%
2052147 1168
 
0.3%

GEMIDDELDE_VERKOOPPRIJS
Real number (ℝ)

MISSING 

Distinct3670
Distinct (%)1.2%
Missing53854
Missing (%)15.5%
Infinite0
Infinite (%)0.0%
Mean3606.8565
Minimum70
Maximum287220
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.7 MiB
2024-01-08T15:06:51.162389image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum70
5-th percentile140
Q1480
median1255
Q34190
95-th percentile13770
Maximum287220
Range287150
Interquartile range (IQR)3710

Descriptive statistics

Standard deviation6540.5049
Coefficient of variation (CV)1.8133532
Kurtosis136.4698
Mean3606.8565
Median Absolute Deviation (MAD)1025
Skewness6.9594877
Sum1.0619632 × 109
Variance42778204
MonotonicityNot monotonic
2024-01-08T15:06:51.363637image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
160 2088
 
0.6%
105 1978
 
0.6%
110 1791
 
0.5%
180 1605
 
0.5%
300 1594
 
0.5%
185 1583
 
0.5%
140 1536
 
0.4%
175 1477
 
0.4%
125 1400
 
0.4%
165 1392
 
0.4%
Other values (3660) 277985
79.8%
(Missing) 53854
 
15.5%
ValueCountFrequency (%)
70 226
 
0.1%
75 75
 
< 0.1%
80 362
 
0.1%
85 919
0.3%
90 670
 
0.2%
95 716
 
0.2%
100 1026
0.3%
105 1978
0.6%
110 1791
0.5%
115 1169
0.3%
ValueCountFrequency (%)
287220 8
< 0.1%
148910 3
 
< 0.1%
142835 4
< 0.1%
122155 4
< 0.1%
116765 3
 
< 0.1%
109725 7
< 0.1%
108570 7
< 0.1%
107655 4
< 0.1%
101270 8
< 0.1%
99590 5
< 0.1%

Interactions

2024-01-08T15:06:42.753995image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-08T15:06:31.028270image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-08T15:06:32.617399image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-08T15:06:34.034417image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-08T15:06:35.460133image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-08T15:06:36.861009image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-08T15:06:38.372481image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-08T15:06:39.841858image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-08T15:06:41.313843image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-08T15:06:42.942183image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-08T15:06:31.289526image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-08T15:06:32.785964image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-08T15:06:34.204206image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-08T15:06:35.626017image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-08T15:06:37.026841image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-08T15:06:38.546642image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-08T15:06:40.016132image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-08T15:06:41.484045image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-08T15:06:43.109138image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-08T15:06:31.450406image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-08T15:06:32.937715image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-08T15:06:34.356074image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-08T15:06:35.778504image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-08T15:06:37.177292image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-08T15:06:38.706946image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-08T15:06:40.172392image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-08T15:06:41.644362image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-08T15:06:43.270837image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-08T15:06:31.617288image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-08T15:06:33.094747image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-08T15:06:34.511189image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-08T15:06:35.931188image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-08T15:06:37.330196image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-08T15:06:38.869996image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-08T15:06:40.334962image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-08T15:06:41.802067image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-08T15:06:43.428807image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-08T15:06:31.781423image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-08T15:06:33.248659image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-08T15:06:34.662523image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-08T15:06:36.080274image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-08T15:06:37.476303image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-08T15:06:39.027205image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-08T15:06:40.494298image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-08T15:06:41.958955image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-08T15:06:43.579830image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-08T15:06:31.937317image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-08T15:06:33.394472image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-08T15:06:34.810278image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-08T15:06:36.223899image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-08T15:06:37.617846image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-08T15:06:39.178805image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-08T15:06:40.646901image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-08T15:06:42.107056image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-08T15:06:43.749780image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-08T15:06:32.109526image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-08T15:06:33.559394image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-08T15:06:34.975164image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-08T15:06:36.385415image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-08T15:06:37.776480image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-08T15:06:39.344536image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-08T15:06:40.816707image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-08T15:06:42.274440image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-08T15:06:43.921148image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-08T15:06:32.281453image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-08T15:06:33.723133image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-08T15:06:35.141497image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-08T15:06:36.547785image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-08T15:06:37.938606image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-08T15:06:39.516097image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-08T15:06:40.985013image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-08T15:06:42.438503image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-08T15:06:44.082406image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-08T15:06:32.449265image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-08T15:06:33.879503image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-08T15:06:35.300157image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-08T15:06:36.704268image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-08T15:06:38.091042image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-08T15:06:39.679269image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-08T15:06:41.146774image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-08T15:06:42.594522image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Missing values

2024-01-08T15:06:44.335659image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-08T15:06:44.839948image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

VERSIEDATUM_BESTANDPEILDATUMJAARBEHANDELEND_SPECIALISME_CDTYPERENDE_DIAGNOSE_CDZORGPRODUCT_CDAANTAL_PAT_PER_ZPDAANTAL_SUBTRAJECT_PER_ZPDAANTAL_PAT_PER_DIAGAANTAL_SUBTRAJECT_PER_DIAGAANTAL_PAT_PER_SPCAANTAL_SUBTRAJECT_PER_SPCGEMIDDELDE_VERKOOPPRIJS
01.02023-12-082023-12-012019-01-0119001199190002438014066619158034179304103932305.0
11.02023-12-082023-12-012019-01-01190011991900020111011736191580341793041039322260.0
21.02023-12-082023-12-012019-01-011900139919000222472173079304103932825.0
31.02023-12-082023-12-012019-01-0119001299190001836494042177532286179304103932440.0
41.02023-12-082023-12-012019-01-011900119919000221259813887619158034179304103932825.0
51.02023-12-082023-12-012019-01-011900129919000061717177532286179304103932NaN
61.02023-12-082023-12-012019-01-011900119919000254087847362619158034179304103932290.0
71.02023-12-082023-12-012019-01-01190013991900025202172173079304103932290.0
81.02023-12-082023-12-012019-01-0119001299190000825812655177532286179304103932460.0
91.02023-12-082023-12-012019-01-011900119919000236061619158034179304103932NaN
VERSIEDATUM_BESTANDPEILDATUMJAARBEHANDELEND_SPECIALISME_CDTYPERENDE_DIAGNOSE_CDZORGPRODUCT_CDAANTAL_PAT_PER_ZPDAANTAL_SUBTRAJECT_PER_ZPDAANTAL_PAT_PER_DIAGAANTAL_SUBTRAJECT_PER_DIAGAANTAL_PAT_PER_SPCAANTAL_SUBTRAJECT_PER_SPCGEMIDDELDE_VERKOOPPRIJS
3482731.02023-12-082023-12-012014-01-01303160129999054221846920470142170018456011230.0
3482741.02023-12-082023-12-012020-01-013137023999901711605887104691526184639560.0
3482751.02023-12-082023-12-012015-01-013623479900620131111117090081597620.0
3482761.02023-12-082023-12-012015-01-0136228199006200822808370900815973005.0
3482771.02023-12-082023-12-012014-01-013032821992990701150685823142170018456014305.0
3482781.02023-12-082023-12-012014-01-0130334428899031115378142170018456015435.0
3482791.02023-12-082023-12-012020-01-013136242909906511448710469152618463350.0
3482801.02023-12-082023-12-012014-01-0130336699035606611189245142170018456012320.0
3482811.02023-12-082023-12-012014-01-0130331429099016112592881421700184560112765.0
3482821.02023-12-082023-12-012014-01-01303838990356056115591142170018456015110.0